Natural language boosts LLM performance in coding, planning, and robotics
Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.
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Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.
Researchers have developed a security solution for power-hungry AI models that offers protection against two common attacks.
A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.
MIT researchers plan to search for proteins that could be used to measure electrical activity in the brain.
FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.
MIT engineers developed a tag that can reveal with near-perfect accuracy whether an item is real or fake. The key is in the glue on the back of the tag.
Researchers developed a simple yet effective solution for a puzzling problem that can worsen the performance of large language models such as ChatGPT.
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.
This new method draws on 200-year-old geometric foundations to give artists control over the appearance of animated characters.